This invention relates to color image processing techniques, and more particularly to a process for filtering the hue component of HSI image data.
Color image processing techniques often are used in image enhancement, video encoding, video editing and computer vision applications. Exemplary techniques include image tracking which relates to the identification of an image object within each a sequence of image frames. Another technique, image segmentation relates to the identification of boundaries and edges of image objects in an image frame.
xe2x80x9cHSIxe2x80x9d refers to the Hue, Saturation, Intensity color model for presenting color data. There are many different color models (also referred to as color domains or color spaces) developed for the representation and manipulation of color data. Color monitors typically use a Red, Green, Blue (RGB) color model. Color printers typically use a Cyan, Yellow, Magenta (CYM) or a Cyan, Yellow, Magenta, Black (CYMK) color model. Color television broadcast signals typically use a luminance, intensity, color difference (YIQ) color model, where I and Q relate to chrominance.
The Hue Saturation Intensity (HSI) color model closely resembles the color sensing properties of human vision. The intensity component is related to the luminance component decoupled from the color. The hue and saturation components are related to the way in which a human perceives color. Such relation to human vision makes it desirable to use the HSI color model for color image processing techniques, such as image enhancement and image segmentation.
The input image data for color image processing techniques typically is in RGB format. Unfortunately the transformation from RGB to HSI color space and from HSI to RGB color space is very nonlinear and complicated in comparison to the conversion formulas among the other color models. As an example, when an RGB image is degraded by random noise, the nonlinearity in the conversion formulae causes the noise distribution in HSI color space to be nonuniform. Further, the noise distribution in HSI color space depends on the intensity and saturation values of the input data. For example, when the intensity value is small, the noise in the saturation and hue is large. This creates problems in using the HSI color model for image processing techniques, such as image enhancement and image segmentation. Accordingly, there is a need for a method which reduces the magnitude of the noise or the nonuniformity of the noise variance in HSI color space.
According to the invention, a method for filtering an angular signal avoids errors in averaging and differencing operations.
According to an aspect of this invention, components of an angular signal (e.g. hue) are separated into groups based upon the sign of a corresponding filter coefficient a, for the respective component and the range of the angular (e.g., hue) component value vi. In one group, the filter component ai is positive and the angular (e.g. hue) component value vi is within the range (0, xcfx80). In another group, the filter component ai is positive and the angular (e.g. hue) component value vi is within the range (xcfx80, 2xcfx80). In yet another group, the filter component ai is negative and the angular (e.g. hue) component value vi is within the range (0, xcfx80). In still another group, the filter component ai is negative and the angular (e.g. hue) component value vi is within the range (xcfx80, 2xcfx80). Note that the signal in each group falls into either (0, xcfx80) or (xcfx80, 2xcfx80). Also, the filter coefficients in each group are either all positive or all negative.
According to another aspect of the invention, a weighted mean is computed for each of the groups, resulting in corresponding computed mean values. The range of angular signals is restricted to be either (0, xcfx80) or (xcfx80, 2xcfx80) so that the rounding effects are avoided. Also, the filter coefficients are restricted to being nonnegative to avoid substraction among multiple angular signals.
According to another aspect of the invention, the weighted mean of the resulting values having positive filter coefficients and the weighted mean of the resulting values having negative filter coefficients are derived. The computed value is redefined if needed to be in modulus 2xcfx80 (e.g., FP=FP MOD 2xcfx80).
According to another aspect of the invention, a weighted distance of the two values (weighted mean for values having positive coefficients and the weighted mean for values having negative coefficients) is derived. The result is the filtered hue value for a given image data pixel. A filtered hue value is obtained in the same manner for each image pixel of a desired image object or of a given image frame.
HSI filtering is applied to an image frame of HSI data to reduce and more uniformly distribute noise while preserving image feature edges. In one implementation for a sequence of image frames, such filtering allows for improved image object tracking ability and improved image object segmentation.
According to another aspect of the invention, a method is provided for segmenting an image frame of pixel data, in which the image frame includes a plurality of pixels. For each pixel of the image frame, the corresponding pixel data is converted into hue, saturation, intensity color space. The HSI pixel data then is filtered. Object segmentation then is performed to define a set of filtered HSI pixel data corresponding to the image object. The image frame then is encoded in which pixel data corresponding to the image object is encoded at a higher bit rate than other pixel data.
An advantage of the invention is that image segmentation techniques are performed in HSI color space where color sensing properties more closely resemble human vision. According to another advantage of this invention, object boundaries are preserved while noise level is significantly reduced and the noise variance is made more uniform.